The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
At the present time there is increasing interest in developing computational capability to automatically design an optimal sensor lay down for border security. In one embodiment the border security application involves detecting illegal human and vehicular border intruders using a set of scanning radars. However, at present there is no satisfactory system-level measure of the detection performance of such radars. If some system-level detection performance existed, then one could use the results of such detection performance to optimize the lay down of the collection of radars over a predefined geographic area, sometimes referred to as an “area of regard” (“AOR”).
It is becoming increasingly clear, both in the academic research community, and in industrial practice, that system configuration design and utilization of sensors requires a good statistical model of the environment and the performance of the sensors being used. One existing solution to evaluate sensor detection performance computes the cumulative detection probability by performing a time consuming Monte-Carlo simulation. The simulation moves intruders on pre-computed paths over the terrain. The single scan detection probability is computed for each periodic scan of a target by simulating the roll of a dice. Additional single-scan detections are sequentially obtained by forwarding the location of the intruder at radar revisit intervals. To overcome false alarms in high clutter environments, typical signal processing pipelines on sensors use detection schemes such as three consecutive or m-of-n single scan detections before externally reporting a detection. To model these, if three consecutive (or m of n) positive outcomes are obtained in the Monte-Carlo simulation at any location, then that location is said to have recorded a detection. Many hundreds of such simulations are repeated to obtain statistically reliable figures on detection probability at all points on the paths.
Note that a significant weakness of the existing method is that detection probabilities are obtained only on points on the paths; there is no detection probability provided over the entire terrain (AOR). Regions outside the specified paths are completely inconsequential to the computation of the metric. This presents a significant technical weakness because detection performance cannot be gauged at all points within an AOR but rather only along the specified paths.
Another drawback with the previously described Monte-Carlo simulation process is that it is time consuming to perform. This makes it difficult (if not impossible) to use such a solution in system design optimization, where possibly many millions of alternate designs may need to be evaluated.